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Job Description
<p><p><b>Required Qualifications :</b><br/><br/>- Master's or Relevant in Computer Science, Machine Learning, or related field<br/><br/>- 6+ years of experience in AIML engineering and research<br/><br/>- Hands-on experience with MLOps, model deployment, and monitoring<br/><br/>- Deep, hands-on experience with FastAPI and Pydantic for building robust, schema-validated microservices<br/><br/>- Experience with experiment tracking tools (e.g., Weights & Biases, MLflow)<br/><br/>- Experience building systems that communicate via message queues like Azure Service Bus<br/><br/>- Experience in explainable AI (AXI) tools - such as LIME and SHAP<br/><br/>- Cloud-Native Development : Solid experience with Docker for applications and deploying orchestrated environments like Kubernetes (AKS)<br/><br/>- In-depth knowledge of LLM vulnerabilities (OWASP Top 10 for LLMs) and experience implementing guardrails against prompt injections and data<br/><br/>- Familiarity with AIML governance, ethics, and responsible AI practices<br/><br/>- Proven expertise in LLMs, generative AI, and deep learning<br/><br/>- Solid programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras)<br/><br/>- Asynchronous & Concurrent Programming : Mastery of Python's asyncio to build scalable, I/O-bound services<br/><br/>- Expert-level Python for Production Systems : Proven ability to architect and write clean, typed, and testable code for distributed systems</p><br/></p> (ref:hirist.tech)
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